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pytorch - 💡(How to fix) Fix [Dynamo] Attempted to call function marked as skipped: torch.sparse_csr_tensor cannot be traced

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Error Message

Original exception: Attempted to call function marked as skipped Explanation: Dynamo does not know how to trace the builtin torch._VariableFunctionsClass.sparse_csr_tensor. This function is either a Python builtin or a third-party C/C++ Python extension

Developer debug context: module: torch, qualname: _VariableFunctionsClass.sparse_csr_tensor, skip reason: <missing reason>

from user code: File "test.py", line 23, in forward sparse_A = torch.sparse_csr_tensor(crow_indices, col_indices, values, size)

Fix Action

Fix / Workaround

Vulnerability Reg file data sampling: Mitigation; Clear Register File

Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl

Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization

Code Example

import torch

@torch.compile(fullgraph=True)
def create_sparse_csr():
    crow_indices = torch.tensor([0, 2, 4])
    col_indices = torch.tensor([0, 1, 0, 1])
    values = torch.tensor([1, 2, 3, 4])
    return torch.sparse_csr_tensor(crow_indices, col_indices, values, size=(2, 2))

# This fails
result = create_sparse_csr()

---

Original exception:
 Attempted to call function marked as skipped
  Explanation: Dynamo does not know how to trace the builtin `torch._VariableFunctionsClass.sparse_csr_tensor.` 
  This function is either a Python builtin or a third-party C/C++ Python extension

  Developer debug context: module: torch, qualname: _VariableFunctionsClass.sparse_csr_tensor, skip reason: <missing reason>

from user code:
   File "test.py", line 23, in forward
    sparse_A = torch.sparse_csr_tensor(crow_indices, col_indices, values, size)
RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

When using torch.sparse_csr_tensor (or torch.sparse_csc_tensor) inside a torch.compile region, Dynamo fails with Attempted to call function marked as skipped. The eager mode executes successfully. code:

import torch

@torch.compile(fullgraph=True)
def create_sparse_csr():
    crow_indices = torch.tensor([0, 2, 4])
    col_indices = torch.tensor([0, 1, 0, 1])
    values = torch.tensor([1, 2, 3, 4])
    return torch.sparse_csr_tensor(crow_indices, col_indices, values, size=(2, 2))

# This fails
result = create_sparse_csr()

output:

Original exception:
 Attempted to call function marked as skipped
  Explanation: Dynamo does not know how to trace the builtin `torch._VariableFunctionsClass.sparse_csr_tensor.` 
  This function is either a Python builtin or a third-party C/C++ Python extension

  Developer debug context: module: torch, qualname: _VariableFunctionsClass.sparse_csr_tensor, skip reason: <missing reason>

from user code:
   File "test.py", line 23, in forward
    sparse_A = torch.sparse_csr_tensor(crow_indices, col_indices, values, size)

Versions

Environment Information PyTorch Build Details:

PyTorch version: 2.10.0.dev20251124+cpu

Is debug build: False

CUDA used to build PyTorch: Could not collect

ROCM used to build PyTorch: N/A

OS and Compilers:

OS: Ubuntu 24.04.1 LTS (x86_64)

GCC version: (Ubuntu 10.5.0-4ubuntu2) 10.5.0

Clang version: 18.1.3 (1)

CMake version: version 3.28.3

Libc version: glibc-2.39

Python Environment:

Python version: 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0] (64-bit runtime)

Python platform: Linux-6.14.0-36-generic-x86_64-with-glibc2.39

Is CUDA available: False

CUDA runtime version: Could not collect

CUDA_MODULE_LOADING set to: N/A

GPU Information:

GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4060 Laptop GPU

Nvidia driver version: 580.95.05

cuDNN version: Could not collect

Is XPU available: False

HIP runtime version: N/A

MIOpen runtime version: N/A

Is XNNPACK available: True

Caching allocator config: N/A

CPU Information:

Architecture: x86_64

CPU op-mode(s): 32-bit, 64-bit

Address sizes: 39 bits physical, 48 bits virtual

Byte Order: Little Endian

CPU(s): 32

On-line CPU(s) list: 0-31

Vendor ID: GenuineIntel

Model name: Intel(R) Core(TM) i9-14900HX

CPU family: 6

Model: 183

Thread(s) per core: 2

Core(s) per socket: 24

Socket(s): 1

Stepping: 1

CPU(s) scaling MHz: 33%

CPU max MHz: 5800.0000

CPU min MHz: 800.0000

BogoMIPS: 4838.40

Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities

Virtualization: VT-x

L1d cache: 896 KiB (24 instances)

L1i cache: 1.3 MiB (24 instances)

L2 cache: 32 MiB (12 instances)

L3 cache: 36 MiB (1 instance)

NUMA node(s): 1

NUMA node0 CPU(s): 0-31

Vulnerability Gather data sampling: Not affected

Vulnerability Ghostwrite: Not affected

Vulnerability Indirect target selection: Not affected

Vulnerability Itlb multihit: Not affected

Vulnerability L1tf: Not affected

Vulnerability Mds: Not affected

Vulnerability Meltdown: Not affected

Vulnerability Mmio stale data: Not affected

Vulnerability Reg file data sampling: Mitigation; Clear Register File

Vulnerability Retbleed: Not affected

Vulnerability Spec rstack overflow: Not affected

Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl

Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization

Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S

Vulnerability Srbds: Not affected

Vulnerability Tsa: Not affected

Vulnerability Tsx async abort: Not affected

Vulnerability Vmscape: Mitigation; IBPB before exit to userspace

Versions of Relevant Libraries:

[pip3] numpy==2.3.3

[pip3] nvidia-cublas-cu12==12.1.3.1

[pip3] nvidia-cuda-cupti-cu12==12.1.105

[pip3] nvidia-cuda-nvrtc-cu12==12.1.105

[pip3] nvidia-cuda-runtime-cu12==12.1.105

[pip3] nvidia-cudnn-cu12==9.1.0.70

[pip3] nvidia-cufft-cu12==11.0.2.54

[pip3] nvidia-curand-cu12==10.3.2.106

[pip3] nvidia-cusolver-cu12==11.4.5.107

[pip3] nvidia-cusparse-cu12==12.1.0.106

[pip3] nvidia-nccl-cu12==2.21.5

[pip3] nvidia-nvjitlink-cu12==12.9.86

[pip3] nvidia-nvtx-cu12==12.1.105

[pip3] optree==0.18.0

[pip3] pytorch-triton-rocm==3.5.0

[pip3] torch==2.10.0.dev20251124+cpu

[pip3] torchao==0.15.0.dev20251124+cpu

[pip3] torchdata==0.12.0.dev20250909+cpu

[pip3] torchtext==0.17.0.dev20240912+cpu

[pip3] triton==3.1.0

[conda] Could not collect

cc @nikitaved @pearu @cpuhrsch @amjames @bhosmer @jcaip @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @kadeng @Lucaskabela @jataylo @azahed98

extent analysis

TL;DR

The issue can be resolved by avoiding the use of torch.sparse_csr_tensor inside a torch.compile region, as Dynamo does not support tracing this function.

Guidance

  • The error message indicates that Dynamo does not know how to trace the torch._VariableFunctionsClass.sparse_csr_tensor function, which is used by torch.sparse_csr_tensor.
  • To mitigate this issue, consider moving the creation of the sparse tensor outside of the torch.compile region.
  • Alternatively, you can try using a different sparse tensor format that is supported by Dynamo, such as torch.sparse_coo_tensor.
  • Verify that the issue is resolved by checking that the code runs without errors and produces the expected output.

Example

import torch

# Move sparse tensor creation outside of torch.compile region
crow_indices = torch.tensor([0, 2, 4])
col_indices = torch.tensor([0, 1, 0, 1])
values = torch.tensor([1, 2, 3, 4])
sparse_tensor = torch.sparse_csr_tensor(crow_indices, col_indices, values, size=(2, 2))

@torch.compile(fullgraph=True)
def create_sparse_csr(sparse_tensor):
    return sparse_tensor

result = create_sparse_csr(sparse_tensor)

Notes

  • This workaround assumes that the sparse tensor is not modified within the torch.compile region.
  • If the sparse tensor needs to be modified, you may need to explore alternative solutions, such as using a different sparse tensor format or avoiding the use of torch.compile altogether.

Recommendation

Apply workaround: Move the creation of the sparse tensor outside of the torch.compile region, as shown in the example above. This should allow the code to run without errors, although it may not be the most efficient solution.

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pytorch - 💡(How to fix) Fix [Dynamo] Attempted to call function marked as skipped: torch.sparse_csr_tensor cannot be traced